Leveraging Uncertainty in Deep Learning for Selective Classification
This work addresses the need for reliable decision-making in deep learning applications, such as reducing errors in image recognition or fraud detection, by providing a practical method for selective classification, though it is incremental in building on existing uncertainty quantification techniques.
The paper tackles the problem of selective classification by proposing a mixed-integer programming framework that leverages model uncertainty and predictive mean to optimize classification and rejection regions, achieving superior performance in non-rejected accuracy and rejection quality on public datasets and outperforming industry standards in cost-sensitive fraud management.
The wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. In this study, we propose a mixed-integer programming framework for classification with reject option (also known as selective classification), that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. Our results indicate superior performance of our framework both in non-rejected accuracy and rejection quality on several publicly available datasets. Moreover, we extend our framework to cost-sensitive settings and show that our approach outperforms industry standard methods significantly for online fraud management in real-world settings.